Heterogeneous vs. Homogeneous System Architectures

I follow a certain philosophy when developing system architectures. I assume that very few systems will ever exist in a consistent form for more than a short period of time. What constitutes a “short period of time” differs depending on the specifics of each system, but in an effort to quantify it, I generally find that it falls somewhere between a week and a month. The driving forces behind the need for an ever changing architecture are largely business requirement based. This is a side effect of the reality that software development, in most cases, is used as a supporting role within the business unit it serves. As business requirements (i.e. additional features, new products, etc.) pour forth, it is the developer’s job to evolve their software system to accommodate these requirements and provide a software based solution to whatever problems lay ahead. Given that many businesses can be identified as having the above characteristics, I can now begin to explain why I believe that Heterogeneous System Architectures hold a significant advantage over Homogeneous System Architectures, in many distributed system cases.

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17 Distributed Systems and Web Scalability Resources

Here's a short list of some great resources that I've found very inspirational and thought provoking. I've broken these resources up into two lists: Blogs and Presentations.

Alternative Memcache Usage: A Highly Scalable, Highly Available, In-Memory Shard Index

While working with Memcache the other night, it dawned on me that it’s usage as a distributed caching mechanism was really just one of many ways to use it. That there are in fact many alternative usages that one could find for Memcache if they could just realize what Memcache really is at its core – a simple distributed hash-table – is an important point worthy of further discussion. To be clear, when I say “simple”, by no means am I implying that Memcache’s implementation is simple, just that the ideas behind it are such. Think about that for a minute. What else could we use a simple distributed hash-table for, besides caching? How about using it as an alternative to the traditional shard lookup method we used in our Master Index Lookup scalability strategy, discussed previously here.

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How to Organize a Database Table’s Keys for Scalability

The key (no pun intended) to understanding how to organize your dataset’s data is to think of each shard not as an individual database, but as one large singular database. Just as in a normal single server database setup where you have a unique key for each row within a table, each row key within each individual shard must be unique to the whole dataset partitioned across all shards. There are a few different ways we can accomplish uniqueness of row keys across a shard cluster. Each has its pro’s and con’s and the one chosen should be specific to the problems you’re trying to solve.

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Scalability Strategies Primer: Database Sharding

This article is a primer, intended to shine some much needed light on the logical, process oriented implementations of database scalability strategies in the form of a broad introduction. More specifically, the intent is to elaborate on the majority of these implementations by example.

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